What are "development test sets" used for?
In machine learning, you normally split your data into 3 parts (80-10-10%).
The first part (80% of your initial data) is for the training of your ML model: this is known as the training dataset.
The second part (10%) is the development set (or dataset), aka validation set. This is used as measuring your performance with various hyperparameters (e.g. in neural networks: layer size).
After you found your best hyperparameters, you learn the model again on the training set, and then test it on your test dataset (10%), which the model has never seen before. Your result on the test data is now a good indicator how your model prediction quality is in the real world (because it was never optimized for this test data).